PLearn::GaussianKernel Class Reference

returns exp(-norm_2(x1-x2)^2/sigma^2) More...

#include <GaussianKernel.h>

Inheritance diagram for PLearn::GaussianKernel:

[legend]Collaboration diagram for PLearn::GaussianKernel:

[legend]List of all members.


Public Member Functions
	GaussianKernel ()
	Default constructor.
	GaussianKernel (real the_sigma)
	Convenient constructor.
	PLEARN_DECLARE_OBJECT (GaussianKernel)
virtual void	build ()
	Should call simply inherited::build(), then this class's build_().
virtual void	makeDeepCopyFromShallowCopy (map< const void , void > &copies)
real	evaluateFromSquaredNormOfDifference (real sqnorm_of_diff) const
real	evaluateFromDotAndSquaredNorm (real sqnorm_x1, real dot_x1_x2, real sqnorm_x2) const
virtual void	setDataForKernelMatrix (VMat the_data)
	This method precomputes the squared norm for all the data to later speed up evaluate methods.
virtual void	addDataForKernelMatrix (const Vec &newRow)
	This method appends the newRow squared norm to the squarednorms Vec field.
virtual real	evaluate (const Vec &x1, const Vec &x2) const
	returns K(x1,x2)
virtual real	evaluate_i_j (int i, int j) const
	returns evaluate(data(i),data(j))
virtual real	evaluate_i_x (int i, const Vec &x, real squared_norm_of_x=-1) const
	returns evaluate(data(i),x)
virtual real	evaluate_x_i (const Vec &x, int i, real squared_norm_of_x=-1) const
	returns evaluate(x,data(i))
virtual void	setParameters (Vec paramvec)
	default version produces an error
Public Attributes
bool	scale_by_sigma
	Build options below.
real	sigma
Static Protected Member Functions
void	declareOptions (OptionList &ol)
	redefine this in subclasses: call declareOption(...) for each option, and then call inherited::declareOptions(options) ( see the declareOption function further down)
Protected Attributes
real	minus_one_over_sigmasquare
	-1 / sigma^2
real	sigmasquare_over_two
	sigma^2 / 2
Vec	squarednorms
	squarednorms of the rows of the data VMat (data is a member of Kernel)
Private Types
typedef Kernel	inherited
Private Member Functions
void	build_ ()

Detailed Description

returns exp(-norm_2(x1-x2)^2/sigma^2)

Definition at line 54 of file GaussianKernel.h.

Member Typedef Documentation

typedef Kernel PLearn::GaussianKernel::inherited [private]

Reimplemented from PLearn::Kernel.
Definition at line 59 of file GaussianKernel.h.

Constructor & Destructor Documentation

PLearn::GaussianKernel::GaussianKernel ( )

Default constructor.

Definition at line 59 of file GaussianKernel.cc.

PLearn::GaussianKernel::GaussianKernel ( real the_sigma )

Convenient constructor.

Definition at line 64 of file GaussianKernel.cc.
References build_().

Member Function Documentation

void PLearn::GaussianKernel::addDataForKernelMatrix ( const Vec & newRow ) [virtual]

This method appends the newRow squared norm to the squarednorms Vec field.

Reimplemented from PLearn::Kernel.
Definition at line 111 of file GaussianKernel.cc.
References PLearn::TVec< T >::length(), PLearn::VMat::length(), PLERROR, PLearn::pownorm(), PLearn::TVec< T >::resize(), and squarednorms.

void PLearn::GaussianKernel::build ( ) [virtual]

Should call simply inherited::build(), then this class's build_().
This method should be callable again at later times, after modifying some option fields to change the "architecture" of the object.
Reimplemented from PLearn::Kernel.
Definition at line 88 of file GaussianKernel.cc.
References build_().

void PLearn::GaussianKernel::build_ ( ) [private]

This method should be redefined in subclasses and do the actual building of the object according to previously set option fields. Constructors can just set option fields, and then call build_. This method is NOT virtual, and will typically be called only from three places: a constructor, the public virtual build() method, and possibly the public virtual read method (which calls its parent's read). build_() can assume that it's parent's build_ has already been called.
Reimplemented from PLearn::Kernel.
Definition at line 97 of file GaussianKernel.cc.
References minus_one_over_sigmasquare, sigma, sigmasquare_over_two, and PLearn::square().
Referenced by build(), and GaussianKernel().

void PLearn::GaussianKernel::declareOptions ( OptionList & ol ) [static, protected]

redefine this in subclasses: call declareOption(...) for each option, and then call inherited::declareOptions(options) ( see the declareOption function further down)
ex: static void declareOptions(OptionList& ol) { declareOption(ol, "inputsize", &MyObject::inputsize_, OptionBase::buildoption, "the size of the input\n it must be provided"); declareOption(ol, "weights", &MyObject::weights, OptionBase::learntoption, "the learnt model weights"); inherited::declareOptions(ol); }
Reimplemented from PLearn::Kernel.
Definition at line 74 of file GaussianKernel.cc.
References PLearn::declareOption(), and PLearn::OptionList.

real PLearn::GaussianKernel::evaluate ( const Vec & x1,

const Vec & x2

) const [virtual]

returns K(x1,x2)

Implements PLearn::Kernel.
Definition at line 155 of file GaussianKernel.cc.
References PLearn::TVec< T >::data(), evaluateFromSquaredNormOfDifference(), PLearn::TVec< T >::length(), PLERROR, and val.

real PLearn::GaussianKernel::evaluate_i_j ( int i,

int j

) const [virtual]

returns evaluate(data(i),data(j))

Reimplemented from PLearn::Kernel.
Definition at line 177 of file GaussianKernel.cc.
References PLearn::endl(), evaluateFromDotAndSquaredNorm(), PLERROR, PLearn::pownorm(), and squarednorms.

real PLearn::GaussianKernel::evaluate_i_x ( int i,

const Vec & x,

real squared_norm_of_x = -1

) const [virtual]

returns evaluate(data(i),x)

Reimplemented from PLearn::Kernel.
Definition at line 200 of file GaussianKernel.cc.
References evaluateFromDotAndSquaredNorm(), PLearn::pownorm(), squarednorms, and x.

real PLearn::GaussianKernel::evaluate_x_i ( const Vec & x,

int i,

real squared_norm_of_x = -1

) const [virtual]

returns evaluate(x,data(i))

Reimplemented from PLearn::Kernel.
Definition at line 226 of file GaussianKernel.cc.
References evaluateFromDotAndSquaredNorm(), PLearn::pownorm(), squarednorms, and x.

real PLearn::GaussianKernel::evaluateFromDotAndSquaredNorm ( real sqnorm_x1,

real dot_x1_x2,

real sqnorm_x2

) const [inline]

Definition at line 90 of file GaussianKernel.h.
References evaluateFromDotAndSquaredNorm().
Referenced by evaluate_i_j(), evaluate_i_x(), evaluate_x_i(), and evaluateFromDotAndSquaredNorm().

real PLearn::GaussianKernel::evaluateFromSquaredNormOfDifference ( real sqnorm_of_diff ) const [inline]

Definition at line 134 of file GaussianKernel.cc.
References PLearn::exp(), minus_one_over_sigmasquare, PLERROR, scale_by_sigma, and sigmasquare_over_two.
Referenced by evaluate().

void PLearn::GaussianKernel::makeDeepCopyFromShallowCopy ( map< const void *, void * > & copies ) [virtual]

Definition at line 104 of file GaussianKernel.cc.
References PLearn::deepCopyField(), and squarednorms.

PLearn::GaussianKernel::PLEARN_DECLARE_OBJECT ( GaussianKernel )

void PLearn::GaussianKernel::setDataForKernelMatrix ( VMat the_data ) [virtual]

This method precomputes the squared norm for all the data to later speed up evaluate methods.

Reimplemented from PLearn::Kernel.
Definition at line 236 of file GaussianKernel.cc.
References PLearn::VMat::length(), PLearn::TVec< T >::resize(), and squarednorms.

void PLearn::GaussianKernel::setParameters ( Vec paramvec ) [virtual]

default version produces an error

Reimplemented from PLearn::Kernel.
Definition at line 247 of file GaussianKernel.cc.
References minus_one_over_sigmasquare, PLWARNING, and sigma.

Member Data Documentation

real PLearn::GaussianKernel::minus_one_over_sigmasquare [protected]

-1 / sigma^2

Definition at line 69 of file GaussianKernel.h.
Referenced by build_(), evaluateFromSquaredNormOfDifference(), and setParameters().

bool PLearn::GaussianKernel::scale_by_sigma

Build options below.

Definition at line 64 of file GaussianKernel.h.
Referenced by evaluateFromSquaredNormOfDifference().

real PLearn::GaussianKernel::sigma

Definition at line 65 of file GaussianKernel.h.
Referenced by build_(), and setParameters().

real PLearn::GaussianKernel::sigmasquare_over_two [protected]

sigma^2 / 2

Definition at line 70 of file GaussianKernel.h.
Referenced by build_(), and evaluateFromSquaredNormOfDifference().

Vec PLearn::GaussianKernel::squarednorms [protected]

squarednorms of the rows of the data VMat (data is a member of Kernel)

Definition at line 72 of file GaussianKernel.h.
Referenced by addDataForKernelMatrix(), evaluate_i_j(), evaluate_i_x(), evaluate_x_i(), makeDeepCopyFromShallowCopy(), and setDataForKernelMatrix().

The documentation for this class was generated from the following files:

Generated on Tue Aug 17 16:23:26 2004 for PLearn by

1.3.7

PLearn::GaussianKernel Class Reference

Public Member Functions

Public Attributes

Static Protected Member Functions

Protected Attributes

Private Types

Private Member Functions

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation